class_docstr: Factory class to create the different tensorflow graphs for ner_dl, generic_classifier, assertion_dl, relation_extraction annotators in spark-nlp healthcare
docstr: Method that return the available tf models in  spark-nlp healthcare
  Examples
  --------
  >>> from sparknlp.training.tfgraphs import tf_graph
  >>> tf_graph.get_models()
docstr_tok: ["Method","that","return","the","available","tf","models","in","spark-nlp","healthcare","Examples","--","--","--","--",">",">",">","from","sparknlp.training.tfgraphs","import","tf_graph",">",">",">","tf_graph.get_models","(",")"]
code: def get_models():
      """
      Method that return the available tf models in  spark-nlp healthcare
      Examples
      --------
      >>> from sparknlp.training.tfgraphs import tf_graph
      >>> tf_graph.get_models()
      """
      return list(TFGraphBuilderFactory.__model_builders.keys())
  
imports: import os
  import re
  from sparknlp.internal import _ResourceHelper_moveFile
  from sparknlp.training._tf_graph_builders.ner_dl.create_graph import create_graph
label_desc: Use image node_id 1 for calling the TFGraphBuilderFactory obj's underlying member method code with example usage: obj.get_models() and returns: list